Vehicular ad hoc networks (VANETs) play a vital role in the success of self-driving\nand semi self-driving vehicles, where they improve safety and comfort. Such vehicles depend\nheavily on external communication with the surrounding environment via data control and\nCooperative Awareness Messages (CAMs) exchanges. VANETs are potentially exposed to a number\nof attacks, such as grey hole, black hole, wormhole and rushing attacks. This work presents an\nintelligent Intrusion Detection System (IDS) that relies on anomaly detection to protect the external\ncommunication system from grey hole and rushing attacks. These attacks aim to disrupt the\ntransmission between vehicles and roadside units. The IDS uses features obtained from a trace\nfile generated in a network simulator and consists of a feed-forward neural network and a support\nvector machine. Additionally, the paper studies the use of a novel systematic response, employed to\nprotect the vehicle when it encounters malicious behaviour. Our simulations of the proposed detection\nsystem show that the proposed schemes possess outstanding detection rates with a reduction in\nfalse alarms. This safe mode response system has been evaluated using four performance metrics,\nnamely, received packets, packet delivery ratio, dropped packets and the average end to end delay,\nunder both normal and abnormal conditions.
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